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The article contains a methodology for social statistics assessing. Thesignificance of minorities (groups that differ in their attributes from themajority) has grown substantially in the modern postindustrial economy andsociety. In the multidimensional characteristics space distribution analysissubjects that not included in the sample can be negligible, but they may bemetrically significant. For example, they can be located compactly and remotelyfrom the main mass of subjects, i.e. have similar characteristics andsignificantly influence the dynamics of socio-economic systems. In addition, itis necessary to evaluate not the probability of errors in each characteristicseparately, but the probability of errors in their combinations. The projectionof a multidimensional space into two-dimensional or three-dimensional space,usually used to analyze such data, leads to the loss of information aboutcompact minorities that merge into a "false majority" or the appearance ofdistribution. Thus, the posed task can not be reliably solved without analysisin a multidimensional space. This problem is especially relevant in statesaggregated social statistics analysis for which relatively small number ofsubjects (countries) are represented. The aim of the article is to developtools for the metric analysis of aggregated social statistics, which ischaracterized by a small number of measurements. It is developed to check theexistence of stable metric and topology structures (clusters) and distributionof countries based on UN data on countries social development characteristics.

Application of the metric data analysis method to social development indicators analysis